Udemy - A Big Data Hadoop and Spark project for absolute beginners

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A Big Data Hadoop and Spark project for absolute beginners [TutsNode.com] - A Big Data Hadoop and Spark project for absolute beginners 8. Advanced Hive
  • 1. Fast queries with Hive Partitioning.mp4 (116.7 MB)
  • 1.1 hive-partition.txt (2.3 KB)
  • 2.1 hive-bucketing.txt (1.2 KB)
  • 2. Fast queries with Hive Bucketing.mp4 (21.0 MB)
  • 2.2 retailstore_large.zip (5.4 MB)
  • 1.2 retailstore_large.zip (5.4 MB)
10. Spark Scala real world coding framework and best practices
  • 14.1 FutureXSparkScalaProject_organize.zip (108.7 KB)
  • 8.2 winutils.zip (36.1 KB)
  • 12.1 FutureXSparkScalaProject_Postgres.zip (12.3 KB)
  • 6.1 ScalaBasics.zip (12.2 KB)
  • 9.1 FutureXSparkScalaProject.zip (11.7 KB)
  • 7.1 SparkHelloWorld.zip (9.7 KB)
  • 5.1 ScalaHelloWorld.zip (8.0 KB)
  • 10.1 Postgres-course-catalog.sql (1.2 KB)
  • 11.1 Postgres-course-catalog_psql.zip (0.6 KB)
  • 8.1 githuhb-link.txt (0.0 KB)
  • 15.1 log4j.zip (0.3 KB)
  • 14. Organizing code with Objects and Methods.mp4 (91.2 MB)
  • 6. Scala basics using IntelliJ.mp4 (75.5 MB)
  • 16. Exception Handling with try, catch, Option, Some and None.mp4 (54.8 MB)
  • 9. Enabling Hive Support in Spark Session.mp4 (46.3 MB)
  • 15. Implementing Log4j SLf4j Logging.mp4 (43.3 MB)
  • 7. Hello World Spark Scala using IntelliJ.mp4 (41.4 MB)
  • 5. Hello World Scala.mp4 (35.1 MB)
  • 13. Importing a project into IntelliJ.mp4 (34.2 MB)
  • 10. Installing PostgreSQL.mp4 (32.8 MB)
  • 12. Fetching PostgresSQL data to a Spark DataFrame.mp4 (31.8 MB)
  • 2. Installing JDK on a local Machine.mp4 (12.7 MB)
  • 11. psql command line interface for PostgreSQL.mp4 (11.0 MB)
  • 8. Configuring HADOOP HOME on Windows using Winutils.mp4 (8.1 MB)
  • 3. Installing IntelliJ IDEA.mp4 (5.2 MB)
  • 4. Adding Scala Plugin to IntelliJ.mp4 (2.6 MB)
  • 1. Spark Scala real world coding introduction.mp4 (2.5 MB)
14. Spark Scala - Structured Streaming
  • 6.1 FuturexMiscSparkScala_Filter.zip (33.7 KB)
  • 2.2 FuturexMiscSparkScala.zip (18.7 KB)
  • 4.2 FuturexMiscSparkScala (1).zip (18.7 KB)
  • 9.1 FuturexMiscSparkScala.zip (7.8 KB)
  • 5.1 StructuredStreamingWindowAggregation.zip (0.8 KB)
  • 8.1 StructuredStreamingWindowAggregation.zip (0.8 KB)
  • 7.1 StructuredStreamingDemoTimestamp.zip (0.7 KB)
  • 5.2 sale.zip (0.5 KB)
  • 2.1 files.zip (0.5 KB)
  • 4.1 files (1).zip (0.5 KB)
  • 6. Filtering Stream.mp4 (44.8 MB)
  • 5. Streaming Aggregation.mp4 (38.7 MB)
  • 8. Aggregation in a time window.mp4 (37.6 MB)
  • 7. Adding timestamp to streaming data.mp4 (30.7 MB)
  • 4. Writing streaming data to a Hive table.mp4 (24.4 MB)
  • 2. Streaming data from files.mp4 (18.8 MB)
  • 3. Batch Vs Streaming code.mp4 (12.6 MB)
  • 9. Tumbling window and Sliding window.mp4 (9.4 MB)
  • 1. Structured Streaming concepts.mp4 (6.1 MB)
13. Running Spark and Hive on a Cloudera QuickStart VM on GCP
  • 4.2 FutureXSparkScalaProject-spark-submit.zip (26.5 KB)
  • 2.1 cloudera-gcp.txt (3.4 KB)
  • 3.1 spark2-cloudera.txt (1.5 KB)
  • 1.1 pom.zip (1.0 KB)
  • 4.1 spark-submit.txt (0.2 KB)
  • 2. Cloudera QuickStart VM Installation on GCP.mp4 (66.1 MB)
  • 1. Exporting the project to an uber jar.mp4 (45.9 MB)
  • 3. Running Spark 2 with Hive on Cloudera QuickStart VM.mp4 (36.6 MB)
  • 5. Doing spark-submit locally.mp4 (26.5 MB)
  • 4. Uber Jar spark-submit on Cloudera QuickStart VM.mp4 (25.0 MB)
3. Hadoop - Hands-On
  • 2.1 retailstore.csv (0.3 KB)
  • 2.2 hive-hdfs-commands.txt (1.5 KB)
  • 2. Storing data in HDFS and querying with Hive.mp4 (82.1 MB)
  • 1. Creating a free Hadoop and Spark cluster using Google Dataproc.mp4 (79.2 MB)
4. Spark concepts and hands-on
  • 5.2 PySpark_DataFrame.zip (17.7 KB)
  • 2.2 spark_installation_on_colab.py (1.3 KB)
  • 4.1 pyspark_rdd.zip (15.7 KB)
  • 4.2 retailstore.csv (0.3 KB)
  • 2.1 Spark_Installation_on_Colab.zip (11.9 KB)
  • 6.1 spark-hadoop-commands.txt (1.8 KB)
  • 5.1 pyspark_dataframe.py (4.5 KB)
  • 3.2 python_basics.py (4.4 KB)
  • 3.1 python_basics.py (4.4 KB)
  • 4. PySpark RDD.mp4 (78.5 MB)
  • 3. Python basics.mp4 (71.3 MB)
  • 5. PySpark - Spark SQL and DataFrame.mp4 (69.4 MB)
  • 6. Running PySpark on a Hadoop Cluster.mp4 (45.4 MB)
  • 2. Installing Spark on Google Colab.mp4 (35.4 MB)
  • 1. Spark concepts.mp4 (28.1 MB)
9. Advanced Spark
  • 2.2 PySpark_udf_and_join.zip (16.0 KB)
  • 3.1 PySpark_udf_and_join.zip (16.0 KB)
  • 2.1 pyspark_udf_and_join.py (3.7 KB)
  • 3.2 pyspark_udf_and_join.py (3.7 KB)
  • 1.1 advanced_spark_datasets.zip (736.2 KB)
  • 3. Joins - Left, Right, Inner, Outer.mp4 (50.0 MB)
  • 2. User Defined Function (UDF).mp4 (29.8 MB)
  • 1. Advanced Spark datasets.mp4 (12.6 MB)
12. Spark Scala Unit Testing using ScalaTest
  • 1.1 FutureXScalaUnitTesting.zip (15.6 KB)
  • 7.1 common.zip (1.7 KB)
  • 9.1 SparkTransformerSpec.zip (0.8 KB)
  • 6.1 SparkTransformerSpec.zip (0.8 KB)
  • 5.1 SparkTransformerSpec.zip (0.7 KB)
  • 8.1 failtests.txt (0.1 KB)
  • 2. Spark Transformation unit testing using ScalaTest.mp4 (73.3 MB)
  • 1. Scala Unit Testing using JUnit & ScalaTest.mp4 (62.4 MB)
  • 5. Throwing Custom Error and Intercepting Error Message.mp4 (60.3 MB)
  • 2.1 FutureXSparkScalaProject_ScalaTest.zip (980.5 KB)
  • 4. Catching Exception using assertThrows.mp4 (23.4 MB)
  • 3. Unit testing to catch an Exception.mp4 (17.6 MB)
  • 6. Testing with assertResult.mp4 (13.0 MB)
  • 7. Testing with Matchers.mp4 (12.1 MB)
  • 9. Sharing fixtures.mp4 (10.9 MB)
  • 8. Failing tests intentionally.mp4 (10.8 MB)

Description


Description

Get started with Big Data quickly leveraging free cloud cluster and solving a real world use case! Learn Hadoop, Hive , Spark (both Python and Scala) from scratch!

Learn to code Spark Scala & PySpark like a real world developer. Understand real world coding best practices, logging, error handling , configuration management using both Scala and Python.

Project

A bank is launching a new credit card and wants to identify prospects it can target in its marketing campaign.

It has received prospect data from various internal and 3rd party sources. The data has various issues such as missing or unknown values in certain fields. The data needs to be cleansed before any kind of analysis can be done.

Since the data is in huge volume with billions of records, the bank has asked you to use Big Data Hadoop and Spark technology to cleanse, transform and analyze this data.

What you will learn :

Big Data, Hadoop concepts
How to create a free Hadoop and Spark cluster using Google Dataproc
Hadoop hands-on – HDFS, Hive
Python basics
PySpark RDD – hands-on
PySpark SQL, DataFrame – hands-on
Project work using PySpark and Hive
Scala basics
Spark Scala DataFrame
Project work using Spark Scala
Spark Scala Real world coding framework and development using Winutil, Maven and IntelliJ.
Python Spark Hadoop Hive coding framework and development using PyCharm
Building a data pipeline using Hive , PostgreSQL, Spark
Logging , error handling and unit testing of PySpark and Spark Scala applications
Spark Scala Structured Streaming
Applying spark transformation on data stored in AWS S3 using Glue and viewing data using Athena

Prerequisites :

Some basic programming skills
Some knowledge of SQL queries

Who this course is for:

Beginners who want to learn Big Data or experienced people who want to transition to a Big Data role
Big data beginners who want to learn how to code in the real world

Requirements

Students should have some programming background and some knowledge of SQL queries.

Last Updated 12/2020



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Udemy - A Big Data Hadoop and Spark project for absolute beginners

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3.7 GB
seeders:14
leechers:13
Udemy - A Big Data Hadoop and Spark project for absolute beginners

Torrent hash: E54E4CD369563B62CD3D61813146CD18318347D1